Multidimensional Particle Swarm Optimization for Machine Learning and Pattern Recognition

Prof. Serkan Kiranyaz worked as a researcher in Nokia Research Center and later in Nokia Mobile Phones in Tampere, Finland. He received his Ph.D. in 2005 and qualified as a Docent in 2007 from the Inst. of Signal Processing of Tampere Univ. of Technology, where he is currently a professor. He is the architect and principal developer of the ongoing content-based multimedia indexing and retrieval framework, MUVIS. His interests include swarm intelligence, stochastic optimization techniques, evolutionary neural networks, content-based multimedia indexing, browsing and retrieval algorithms, audio analysis and audio-based multimedia retrieval, object extraction, and biomedical signal analysis. Dr. Turker Ince received his Ph.D. from the Univ. of Massachusetts, Amherst, in 2001 in electrical engineering. He was a research assistant in the Microwave Remote Sensing Laboratory of UMass-Amherst from 1996 to 2001, and he worked as a design engineer at Aware, Inc., Boston from 2001 to 2004, and at Texas Instruments, Inc., Dallas from 2004 to 2006. He is currently an associate professor in the Dept. of Electrical and Electronics Engineering of Izmir University of Economics, Turkey. He teaches and conducts research in the areas of remote sensing, radar systems and signal processing, neural networks, and evolutionary optimization. Prof. Moncef Gabbouj received his Ph.D. from Purdue University in 1989 in electrical engineering. He is an Academy Professor with the Academy of Finland (2011-2015), and a Professor in the Dept. of Signal Processing of Tampere University of Technology, Finland. He is a Fellow of the IEEE, he has chaired many research and education projects and technical committees, and he has edited related journal issues. His interests include multimedia content-based analysis, indexing and retrieval, swarm optimization, nonlinear signal and image processing and analysis, voice conversion, and video processing and coding. He has coauthoredover 500 publications.

From the book reviews: "This book has enough material to be used as a reference text in research in areas of biomedical signal processing, classification, and clustering. Alternatively, it can be employed as an extra textbook in a graduate course on optimization. Its clear style and strong practical orientation make the book an excellent addition to the bookshelf of any researcher dealing with optimization problems in many dimensions." (Alexander Tzanov, Computing Reviews, July, 2014)

• Chap. 1 Introduction.- Chap. 2 Optimization Techniques.- Chap. 3 Particle Swarm Optimization.- Chap. 4 Multidimensional Particle Swarm Optimization.- Chap. 5 Improving Global Convergence.- Chap. 6 Dynamic Data Clustering.- Chap. 7 Evolutionary Artificial Neural Networks.- Chap. 8 Personalized ECG Classification.- Chap. 9 Image Classification Through a Collective Network of Binary Classifiers.- Chap. 10 Evolutionary Feature Synthesis for Image Retrieval.